Aarhus May 2017 - Life History of Foraging in 39 Human Societies

Talk given at Interacting Minds Centre in Aarhus, Denmark on 2017-05-30. This is draft work, and details of result may yet change before publication.

Transcript

1. 7 29 1 2 3 6 4 5 10 9 1213 15 14 27 34 30 33 32 31 35 36 38 39 37 28 26 25 24 21 16 19 23 20 18 17 22 8 11 tundra boreal forest temperate forest desert grassland shrubland savanna rain forest A Life History of Human Foraging in 39 Societies Richard McElreath, MPI for Evolutionary Anthropology, Leipzig Jeremy Koster, University of Cincinnati & our many collaborators
2. Elephant embryo, 16 weeks
3. Sturnus vulgaris
7. Long-form Adaptation
12. Kuzawa et al 2014 Metabolic costs and evolutionary implications of human brain development Males Females Brain Body Brain Body
13. chil esti in-p and sex son ious and Haw data mat que food food mea larg A tura sho erer par juve rate mal crea in a Figure 3. The mean expected daily energy consumption per individual for each age-sex Kaplan, Hill, Lancaster, Hurtado 2000
14. gogo for both sexes combined compared to that of: a) other wild chimpanzee communities and b) human hunter-gatherer p e tables (Kanyawara: Muller and Wrangham, 2014, their Table 1; Taï all periods: Hill et al., 2001; Taï stable period: Boesch and , their Table 3; Gombe: Bronikowski et al., 2016; Ach e forest period: Hill and Hurtado, 1996; Hadza: Blurton Jones, 2016, th owell, 2010). B.M. Wood et al. / Journal of Human Evolution 105 (2017) 41e56
15. A model for brain life history evolution brain reproductive body skill Mauricio González-Forero et al 2017 A model for brain life history evolution
16. Summary of the borderlands • Humans grow very slow • Brains grow very fast • Adult skill & surplus • How big is the surplus? • How fast does adult skill develop? • How general is pattern? • How much do pops & individuals vary?
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18. 7 29 1 2 3 6 4 5 10 9 1213 15 14 27 34 30 33 32 31 35 36 38 39 37 28 26 25 24 21 16 19 23 20 18 17 22 8 11
19. ᭧ 2009 by The Wenner-Gren Foundation for Anthropological Research. All rights reserved. DOI: 10.1086/597981 Supplement A from Hill and Kintigh, “Can Anthropologists Distinguish Good and Poor Hunters? Implications for Hunting Hypotheses, Sharing Conventions, and Cultural Transmission” (Current Anthropology, vol. 50, no. 3, p. 369) Online Figures 1978 2009 Figure A2. A, Photo taken in January 1978; B, photo taken in January 2009. Reports Can Anthropologists Distinguish Good and Poor Hunters? Implications for Hunting Hypotheses, Sharing Conventions, and Cultural Transmission Kim Hill and Keith Kintigh From:
20. Goals & sample • Goals • Does pattern hold? • How variable? • Develop stats machinery • Sample • 39 sites • 1821 individuals • 21160 trips • 23747 harvests • uncountable headaches
21. Our pre-specified model 0 20 40 60 80 0 1 age 'knowledge' 0 20 40 60 80 0 1 age 'capacity' 0 20 40 60 80 0 age 'skill' × = S(x, k, m, b) = exp( mx) 1 exp( kx) b
22. Our pre-specified model × = S(x, k, m, b) = exp( mx) 1 exp( kx) b age “skill” )19
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23. From skill to production labor success rate harvest size expected returns labor labor 2 5 10 1 10 1 1 5 variation due to skill variation due to group size Production = SkillA LaborB TechnologyC
24. Simple model, massively multilevel 0 20 40 60 80 0 age 'skill' 0 20 40 60 80 0 age 'skill' 0 20 40 60 80 0 age 'skill' 0 20 40 60 80 0 age 'skill' Site 1 0 20 40 60 80 0 age 'skill' 0 20 40 60 80 0 age 'skill' 0 20 40 60 80 0 age 'skill' 0 20 40 60 80 0 age 'skill' Site 2 0 20 40 60 80 0 age 'skill' 0 20 40 60 80 0 age 'skill' 0 20 40 60 80 0 age 'skill' 0 20 40 60 80 0 age 'skill' Site 3 0 20 40 60 80 0 age 'skill' 0 20 40 60 80 0 age 'skill' 0 20 40 60 80 0 age 'skill' 0 20 40 60 80 0 age 'skill' Site n 0 20 40 60 80 0 age 'skill' 0 20 40 60 80 0 age 'skill' Pooling within 0 20 40 60 80 0 age 'skill' 0 20 40 60 80 0 age 'skill' Pooling between 0 20 40 60 80 0 age 'skill'
25. Simple model, complex data • Diligence and duty: • Missing data (labor, technology) • Measurement error (age) • Solutions (no excuses): • Imputation • Marginalization
26. “Pooh?” said Piglet. “Yes, Piglet?” said Pooh. “27417 parameters,” said Piglet. “Oh, bother,” said Pooh.
28. How & what happened • How? • mc-stan.org (HMC) • Tested via simulation • Regularizing priors • Prior sensitivity checks • What happened? • General patterns • Sources of variation • Needs and lessons 100 150 200 250 300 350 400 1.2 1.6 2.0 n_eff = 437 lifehistmeans[1] 100 150 200 250 300 350 400 -0.4 0.0 0.4 n_eff = 406 lifehistmeans[2] 100 150 200 250 300 350 400 -0.2 0.4 1.0 n_eff = 520 lifehistmeans[3] 100 150 200 250 300 350 400 -1.5 0.0 1.5 n_eff = 1711 lifehistmeans[4] 100 150 200 250 300 350 400 -0.5 0.5 n_eff = 1311 lifehistmeans[5]
29. 0 40 80 38 15 ACH 147 (14364) 0 40 80 38 15 success 147 (14364) 0 40 80 38 15 harvest 147 (14364) 0 40 80 38 15 production 147 (14364)
30. 0 40 80 24 0 --- ids (obs) peak obs range 0 40 80 30 1 CRE 16 (127) 0 40 80 32 2 MYA 59 (464) 0 40 80 32 3 MYN 52 (359) 0 40 80 30 4 QUI 32 (189) 0 40 80 32 5 WAO 48 (373) 0 40 80 29 6 BAR 18 (233) 0 40 80 30 7 INU 15 (29) 0 40 80 24 8 MTS 69 (1441) 0 40 80 29 9 PIR 42 (274) 0 40 80 32 10 CLB 12 (45) 0 40 80 28 11 PME 23 (172) 0 40 80 31 12 TS1 29 (127) 0 40 80 31 13 TS2 37 (139) 0 40 80 32 14 TS3 168 (793) 0 40 80 38 15 ACH147 (14364) 0 40 80 34 16 GB1 69 (488) 0 40 80 30 17 GB2 4 (37) 0 40 80 30 18 GB3 16 (73) 0 40 80 31 19 CN1 6 (80) 0 40 80 31 20 GB4 19 (114) 0 40 80 28 21 BK1 80 (249) 0 40 80 28 22 BK2 57 (114) 0 40 80 32 23 CN2 14 (78) 0 40 80 33 24 CN3 15 (67) 0 40 80 28 25 BFA 59 (433) 0 40 80 29 26 CN4 14 (287) 0 40 80 37 27 BIS 24 (231) 0 40 80 31 28 HEH 45 (45) 0 40 80 33 29 DLG 26 (76) 0 40 80 34 30 BTK 27 (268) 0 40 80 29 31 PN1 35 (119) 0 40 80 30 32 PN2 23 (125) 0 40 80 29 33 AGT 44 (211) 0 40 80 31 34 MRT 77 (758) 0 40 80 31 35 NUA 36 (140) 0 40 80 33 36 NIM 26 (180) 0 40 80 31 37 NEN 7 (7) 0 40 80 32 38 MAR 6 (28) 0 40 80 24 39 WOL 27 (410) Skill (raw)
31. 0 40 80 23 0 --- ids (obs) peak obs range 0 40 80 33 1 CRE 16 (127) 0 40 80 32 2 MYA 59 (464) 0 40 80 32 3 MYN 52 (359) 0 40 80 29 4 QUI 32 (189) 0 40 80 32 5 WAO 48 (373) 0 40 80 31 6 BAR 18 (233) 0 40 80 32 7 INU 15 (29) 0 40 80 27 8 MTS 69 (1441) 0 40 80 31 9 PIR 42 (274) 0 40 80 31 10 CLB 12 (45) 0 40 80 31 11 PME 23 (172) 0 40 80 32 12 TS1 29 (127) 0 40 80 32 13 TS2 37 (139) 0 40 80 33 14 TS3 168 (793) 0 40 80 38 15 ACH147 (14364) 0 40 80 34 16 GB1 69 (488) 0 40 80 33 17 GB2 4 (37) 0 40 80 32 18 GB3 16 (73) 0 40 80 36 19 CN1 6 (80) 0 40 80 31 20 GB4 19 (114) 0 40 80 31 21 BK1 80 (249) 0 40 80 29 22 BK2 57 (114) 0 40 80 35 23 CN2 14 (78) 0 40 80 34 24 CN3 15 (67) 0 40 80 30 25 BFA 59 (433) 0 40 80 30 26 CN4 14 (287) 0 40 80 40 27 BIS 24 (231) 0 40 80 32 28 HEH 45 (45) 0 40 80 35 29 DLG 26 (76) 0 40 80 36 30 BTK 27 (268) 0 40 80 30 31 PN1 35 (119) 0 40 80 32 32 PN2 23 (125) 0 40 80 31 33 AGT 44 (211) 0 40 80 34 34 MRT 77 (758) 0 40 80 32 35 NUA 36 (140) 0 40 80 30 36 NIM 26 (180) 0 40 80 31 37 NEN 7 (7) 0 40 80 31 38 MAR 6 (28) 0 40 80 27 39 WOL 27 (410) Skill (samples)
32. 0 40 80 26 0 --- ids (obs) peak obs range 0 40 80 30 1 CRE 16 (127) 0 40 80 32 2 MYA 59 (464) 0 40 80 32 3 MYN 52 (359) 0 40 80 30 4 QUI 32 (189) 0 40 80 32 5 WAO 48 (373) 0 40 80 28 6 BAR 18 (233) 0 40 80 30 7 INU 15 (29) 0 40 80 24 8 MTS 69 (1441) 0 40 80 29 9 PIR 42 (274) 0 40 80 32 10 CLB 12 (45) 0 40 80 29 11 PME 23 (172) 0 40 80 31 12 TS1 29 (127) 0 40 80 31 13 TS2 37 (139) 0 40 80 32 14 TS3 168 (793) 0 40 80 38 15 ACH 147 (14364) 0 40 80 34 16 GB1 69 (488) 0 40 80 30 17 GB2 4 (37) 0 40 80 30 18 GB3 16 (73) 0 40 80 31 19 CN1 6 (80) 0 40 80 32 20 GB4 19 (114) 0 40 80 28 21 BK1 80 (249) 0 40 80 28 22 BK2 57 (114) 0 40 80 32 23 CN2 14 (78) 0 40 80 33 24 CN3 15 (67) 0 40 80 28 25 BFA 59 (433) 0 40 80 29 26 CN4 14 (287) 0 40 80 39 27 BIS 24 (231) 0 40 80 31 28 HEH 45 (45) 0 40 80 32 29 DLG 26 (76) 0 40 80 34 30 BTK 27 (268) 0 40 80 29 31 PN1 35 (119) 0 40 80 30 32 PN2 23 (125) 0 40 80 29 33 AGT 44 (211) 0 40 80 31 34 MRT 77 (758) 0 40 80 31 35 NUA 36 (140) 0 40 80 34 36 NIM 26 (180) 0 40 80 31 37 NEN 7 (7) 0 40 80 32 38 MAR 6 (28) 0 40 80 23 39 WOL 27 (410) Production
33. 0 40 80 An average site 18 55 31 18yo has 86% of max skill. 18yo has same skill as 55yo.
34. General patterns • Peak skill after peak size • Peak often very flat => skill(18) is 86% max • Decline slow => skill(55) approx skill(18) • Production oft declines more rapidly (skill thresholds?) • Value: Pace of energy supply & demand 0 40 80
35. 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 age (standardized) probability failure Forager 1329(9) 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.5 1.0 1.5 2.0 2.5 age (standardized) harvest (standardized) 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 age (standardized) probability failure Forager 1336(9) 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.5 1.0 1.5 2.0 2.5 age (standardized) harvest (standardized)
36. 0.0 0.5 1.0 1.5 2.0 0 1 2 3 standard deviation among individuals Density growth (k) decline (m) 0.0 0.5 1.0 1.5 2.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 standard deviation among societies Density growth (k) decline (m) Site variation Individual variation
37. Variation • Data insufficient for strong conclusions • Substantial individual differences • Individual variation <= rates of loss • Individual differences structure sharing economy • Site variation <= rates of gain • Site differences about early life, individual differences about late life? 0 40 80 38 15 production 147 (14364) 0 40 80
38. Outlook • Confirmed patterns: • Peak after maturity • Productive after reproduction • Individual differences substantial • Delayed growth pays (conditional on cultural evolution) • Clearer picture of maintenance • Very unclear how to evolve • Estimates feed into models • Need new, long-term studies